International Trade and Growth in Nigeria

International Trade and Growth in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article International Trade and Growth in Nigeria Quadri Adeniji, Rasaki Olufemi Kareem, Abdulqodir Babtunde Taiwo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5904994/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study investigates the relationship between international trade and economic growth in Nigeria from 1981 to 2022. The specific objective was to assess the impact of total imports, and total exports on per capita income. Data on total imports, exports (proxies for international trade), and per capita income (proxy for economic growth) were obtained from the World Bank Development Indicators (WDI). The stationarity of the data was tested using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The appropriate lag lengths were determined using lag length selection criteria. The ARDL bound test was employed to assess the long-run relationships among the variables. The Error Correction Model (ECM) was applied to estimate the coefficients in the model. The results of the ADF and PP tests showed that the variables were stationary at mixed levels, both at level I 0 and first difference I 1 . A one-period lag was selected based on the lag length criteria. The ARDL bound test revealed significant long-run equilibrium relationships in the Model. The ECM results revealed that imports had an insignificant negative effect on per capita income (coefficient = -0.017954, p = 0.4627), while exports had an insignificant positive effect (coefficient = 0.004460, p = 0.8352). The study concluded that international trade had an insignificant impact on Nigeria's economic growth over the study period and recommends that the government should implement trade policy reforms that simplify procedures and reduce trade barriers to expand market access and increase Nigeria’s integration into global supply chains. Imports exports trade barrier per-capita income Figures Figure 1 Figure 2 Introduction International trade has long been recognized as a key driver of economic growth, dating back to the mercantilist era, where countries exchanged goods, services, and capital, laying the foundation for modern economies (Krugman & Obstfeld, 2020 ). The classical theories of(Smith, 1776 ) and(Ricardo, 1821 ) emphasized the benefits of specialization and comparative advantage, both of which remain relevant in today’s globalized economy. Globalization has further amplified the importance of trade, spreading technology, fostering competition, and promoting industrial specialization, thereby stimulating economic development (Heckscher & Ohlin, 1991 ). These theoretical frameworks highlight how trade can promote growth, but its effects vary depending on country-specific factors like institutional frameworks, infrastructure, and industrial policies (Rodrik, 2018 ) In nations like China and India, trade has contributed significantly to rapid economic growth, helping them transition from agrarian-based economies to major global players, thus lifting millions out of poverty (Lin, 2001 ). However, the same cannot be said for many sub-Saharan African countries, including Nigeria, which despite participating in global trade, have not seen comparable levels of economic growth. This raises concerns about whether trade alone can serve as a reliable tool for development in such regions (Collier, 2007 ). In Nigeria, international trade, particularly in the oil and agricultural sectors, has played a significant role in generating revenue. Before the discovery of oil, Nigeria relied heavily on agricultural exports such as palm oil, groundnuts, and cocoa. However, the focus has since shifted to oil, contributing to a decline in agricultural productivity and a growing dependency on crude oil exports (Ezindu, Nkechi, Victoria, & Chike, 2020 ). Despite its potential to spur growth, Nigeria’s overreliance on crude oil and the high volume of imports, particularly of machinery, equipment, and food products, has left the country vulnerable to global market fluctuations. Trade liberalization, widely promoted by the World Trade Organization (WTO) and the International Monetary Fund (IMF), is believed to reduce barriers and boost productivity (International Monetary Fund, 2020; World Trade Organization, 2018). However, for countries like Nigeria, where weak institutions, erratic exchange rates, and infrastructure deficits persist, the anticipated benefits of liberalized trade have been inconsistent (Stiglitz, 2002 ). This study aims to analyze the impact of international trade on Nigeria's economic growth while exploring factors such as political stability, institutional quality, and infrastructure that may shape the trade-growth relationship. (Acemoglu, Johnson, & Robinson) suggest that countries with strong institutions are better positioned to harness the benefits of international trade by integrating into global value chains. Conversely, Rodrik ( 2011 ) argues that countries with weak institutions and inadequate infrastructure may struggle to compete globally, thereby missing out on the benefits of trade liberalization. The analysis in this study focuses solely on Nigeria, using secondary data on economic growth proxy for GDP per capita income and international trade proxy with total imports and exports covering the period from 1981 to 2022. To obtain robust estimation results, the study will utilize relevant econometric methodologies that account for the complexities of time-series data. This study reviews existing literature on international trade and growth, draws on both theoretical and empirical contributions, and presents a detailed analysis of the data and methodology. The findings will offer recommendations for strengthening Nigeria’s position in global trade, fostering long-term economic development, and reducing its reliance on volatile sectors. Literature Review Conceptual Review International Trade Abebefe ( 1995 ) stated that trade involves multiple exchanges of products carried out through market interactions. Transactions occurring beyond the jurisdiction of a sovereign state are considered international. Similarly, Nordhaus ( 2002 ) argued that the system in which countries import and export products, capital, and services is referred to as international trade. They differentiate between domestic and global trade based on increased trade possibilities, sovereign nations, and exchange rates, highlighting their practical and economic significance. Foreign trade is driven by the encouragement of specialization, leading to increased production (Ingram & Dunn, 1993 ; Nordhaus, 2002 ). Mannur ( 1995 ) posited that international trade involves the exchange of products and services among citizens of different countries, serving as a means to facilitate global service flows, trade in goods, and factor movements. It is based on the understanding that no single nation can provide all the goods and services its population requires due to resource limitations and disparities. Similarly, Classical and neo-classical economists viewed foreign trade as an integral to a country's development process and as a source of growth. With globalization and international trade, nations have become increasingly interconnected in recent years. Afolabi, Danladi, and Azeez ( 2017 ) noted that foreign trade is the most significant and enduring aspect of a country's international economic relations. Concepts of Growth Lipsey ( 1986 ) defined growth as a long-term upward trend in a nation's total output. This indicates a sustained increase in Gross Domestic Product (GDP) over an extended period. GDP is a measure of an economy's total output of goods and services, is commonly used to describe economic growth. In other to represent the real value of an economy, GDP is adjusted for inflation to gauge economic growth accurately. This adjustment, known as rebasing, was carried out by Nigeria in 2015 to account for inflation's effects and provide precise measures of growth over time. Economic growth is quantified by increases in the quantity of goods and services over time. Empirical Review Several efforts have been made to investigate the connection between global trade and economic growth empirically, and the findings of these studies have been inconsistent. For instance, Shido-Ikwu et al. ( 2023 ) investigate the impact of international trade on Nigeria's economic growth from 1981 to 2019, utilizing the Autoregressive Distributive Lag (ARDL) approach. They discovered a long-run relationship between the variables through the ARDL bound test approach, the short-run and long-run estimations indicate that import trade, foreign direct investment (FDI), and the exchange rate have a negative and insignificant impact on Nigeria's economic growth. However, export trade shows a direct and significant impact on economic growth during the study period. The study concludes that international trade had an insignificant impact on Nigeria's economic growth over the period under review. Similarly, Sun and Heshmati (2010) explore the role of international trade in China's economic growth, highlighting its increasingly significant contribution. The study begins by reviewing the concepts and evolution of China's international trade regime, as well as the policies favoring trade sectors. The research extensively analyzes China's international trade performance and evaluates its effects on economic growth, focusing on productivity improvement. Both econometric and non-parametric approaches are employed using a 6-year balanced panel data of 31 Chinese provinces from 2002 to 2007. In the econometric approach, a stochastic frontier production function is estimated to identify province-specific determinants of inefficiency in trade. Meanwhile, the non-parametric approach calculates the Divisia index for each province/region to serve as a benchmark. The study concludes that increased participation in global trade has enabled China to realize both static and dynamic benefits, leading to rapid national economic growth. Both the volume of international trade and the trade structure, particularly towards high-tech exports, have positive effects on China's regional productivity. However, the eastern region of China has seen the most rapid development, while the central and western provinces have lagged behind in both economic growth and international trade participation. Emehelu ( 2021 ) conducted an empirical analysis of the impact of international trade on Nigeria's economic growth over the period 1981–2018, employing the Ordinary Least Squares (OLS) technique. The research investigated the effects of exchange rates, trade policy changes, and the influence of trade liberalization on Nigeria's economic growth. The study employed independent variables such as policy changes (dummy), exchange rates, and liberalization/openness, regressing them on the real Gross Domestic Product (GDP) of Nigeria using secondary data from the Central Bank of Nigeria Statistical Bulletin 2018. Econometric diagnostics were conducted to check for unit roots in the series using the Augmented Dickey-Fuller technique. The tests indicated that the variables were integrated at order 1(1). The Johansen co-integration test was also performed to assess the long-run relationship among the variables, confirming the absence of long-run equilibrium. The study's findings revealed a negative and insignificant relationship between exchange rates and economic growth in Nigeria. Moreover, various trade policies in Nigeria were found to negatively and significantly impact GDP growth, hindering economic prosperity. As a result, the study recommends that, given the limited significant effects of import and export trade on Nigeria's growth, the federal government should implement programs and policies that promote local production while discouraging the importation of specific essential products. Similarly, Omoke and Opuala–Charles ( 2021 ) considered institutional quality and investigated the relationship between trade openness and Nigeria's economic growth. They utilized indices of trade openness, such as total trade, import trade, and export trade, spanning from 1984 to 2017. The study determined the long-run relationship using the ARDL bounds testing technique and discovered that import trade had a significant negative influence on economic growth, while export trade had a significant positive impact on economic growth, aligning with prior expectations. Additionally, the results indicated a negative impact of import trade on economic growth in the long-term when institutional quality in Nigeria was less pronounced. The study concluded that the benefits of trade can be channeled towards initiatives promoting economic growth, particularly with the support of strong institutions and good governance. Likewise, Yusuff, Adekanye, and Babalola ( 2020 ) investigated how foreign trade affects the expansion of the Nigerian economy from 1986 to 2017 using Ordinary Least Squares (OLS) estimation techniques. Their results showed a negative connection between foreign trade and GDP per capita during the study period. The study recommended that the government implement crucial trade-oriented policies to stimulate economic growth through increased exports and accumulation of more foreign revenues to drive production growth in the nation. Methodology Theoretical framework The theoretical framework of this study is anchored on the comparative advantage theory propounded by David Ricardo in 1817. According to the theory, countries should specialize in producing goods and services where they have a lower opportunity cost than other nations, enabling them to efficiently allocate their resource, leading to increased productivity and output. As countries trade based on their comparative advantages and focus on what can optimize their production and stimulate innovations, they can access a wider range of goods and services at lower costs, fostering economic growth. Model specification In other to achieve the objective of this study. The research establishes a functional relationship between international trade and economic growth as stated implicitly below: \(\:Economic\:growth=f(\:international\:trade\) ) (1) The study proxies economic growth with GDP per capita as the dependent variable while international trade is decomposed into total import, total export, and real exchange rate and FDI as a control variable due to their significant impact on economic growth through its influence on export competitiveness, import prices, and FDI role in enhancing capital flow and technology transfer that boost economic growth. The explicit form of Eq. 1 is specified below: $$\:\text{P}\text{C}\text{I}=f(Import,\:Export,\:Exchange\:rate,\:FDI)$$ 2 econometrically $$\:{PCI}_{t}={\alpha\:}_{0}+{\alpha\:}_{1}{IMP}_{t}+{\alpha\:}_{2}{EXP}_{t}+{\alpha\:}_{3}{EXR}_{t}+{\alpha\:}_{4}{\text{F}\text{D}\text{I}}_{t\:}+\:{\text{ϵ}}_{t}$$ 3 Taking the log of Eq. 3 we have: $$\:{InPCI}_{t}={\alpha\:}_{0}+{\alpha\:}_{1}{InIMP}_{t}+{\alpha\:}_{2}In{EXP}_{t}+{\alpha\:}_{3}{EXR}_{t}+{\alpha\:}_{4}{\text{I}\text{n}\text{F}\text{D}\text{I}}_{t\:}+{\text{ϵ}}_{t}$$ 4 PCI – Per Capita Income in thousands of Naira IMP- total import in millions of Naira EXP- total export in millions of Naira EXR- real exchange rate in \(\:\raisebox{1ex}{$\text{N}$}\!\left/\:\!\raisebox{-1ex}{$\text{\$}$}\right.\) \(\:{\alpha\:}_{0}\:\) intercept; \(\:{\alpha\:}_{1-4}\) coefficients to be estimated In- natural logarithm; t 1981–2023 ε- error term Source of data Secondary data on PCI, real exchange rate, and FDI from 1981 to 2023 is sourced from the World Development Indicator, while total imports and exports were sourced from the Central Bank of Nigeria Statistical Bulletin. Method of data analysis Descriptive statistics such as mean, mode, and median are adopted to determine the behavior of the data as well as the Jarque-Bera, and skewness statistics to ascertain the normal distribution. Due to the unpredictable movement of time series data, the study employ the Augmented Dickey-Fuller (ADF) and Philip Perron stationary test to determine the level of stationarity of the data. The study determines the appropriate lag length selection criterion in other to ascertain the appropriate lag period for the analysis. The investigation extends to determining the long-run relationship among variables through the Autoregressive Distributed Lag (ARDL) bound test. The study employed the Error Correction Model (ECM). To ensure robustness, the study conducts the tests for heteroscedasticity and serial autocorrelation to scrutinize potential issues in the residuals of the regression in three models. The ECM is specified below: $$\:\varDelta\:{InPCI}_{t}={\alpha\:}_{0}+{\alpha\:}_{1}{\varDelta\:InPCI}_{t-1}+{\alpha\:}_{2}{\varDelta\:InIMP}_{t}+{\alpha\:}_{3}\varDelta\:In{EXP}_{t}+{\alpha\:}_{4}{\varDelta\:EXR}_{t}+{\alpha\:}_{5}\varDelta\:{InFDI}_{t\:}+{\varphi\:}_{1}\varDelta\:{ECT}_{t-1}+{ϵ}_{t2}$$ 5 In Eq. 5 , the coefficient of \(\:{{\upvarphi\:}}_{1}\) , is expected to be negative and statistically significant to confirm the presence of a long-run equilibrium relationship. While \(\:\:{\alpha\:}_{1-5}\) are short-run coefficients that capture the immediate impact of changes in international trade on economic growth within the period under study. Data analysis and Presentation of Findings Summary of Statistics Table 1 Result of Descriptive Statistics EXR FDI EXP IMP PCI Mean 115.74 376 6227897 5194325 270179.8 Median 15.26 125 1906839 1435438 245112.9 Maximum 25.98 1,360 27251572 27115109 379251.6 Minimum 0.61 -79.6 7502.5 5983.6 199311.3 Std. Dev. 19.14 448 7243131 7108949 65607.04 Skewness 1.02 0.784806 0.99 1.528542 0.38 Kurtosis 3.22 2.213611 3.05 4.523062 1.48 Jarque-Bera 7.39 5.39 6.91 20.41 5.07 Probability 0.02 0.07 0.03 0.00 0.08 Sum 4861.13 1.58E + 13 2.62E + 08 2.18E + 08 11347551 Sum Sq. Dev. 581975.7 8.21E + 24 2.15E + 15 2.07E + 15 1.76E + 11 Observations 42 42 42 42 42 Source: Author’s Computation, 2024 Table 1 presents the descriptive statistics for the exchange rate (EXR \(\:\raisebox{1ex}{$\text{N}$}\!\left/\:\!\raisebox{-1ex}{$\text{\$}$}\right.\) ), foreign direct investment (FDI in Naira), exports (EXP in Naira), imports (IMP in Naira), and per capita income (PCI in Naira). The mean exchange rate (EXR) is 115.74 Naira, reflecting the average value of the Naira over the period. Foreign direct investment (FDI) averages 376 million Naira, while exports and imports are, on average, 6.23 billion Naira and 5.19 billion Naira, respectively. Per capita income (PCI) has a mean of 270,179.8 Naira, suggesting significant economic variation among the population. The median values further highlight this variability, with the exchange rate at 15.26 Naira, FDI at 125 million Naira, and exports and imports at 1.91 billion Naira and 1.44 billion Naira, respectively. The maximum and minimum values show extreme fluctuations, particularly in exports and imports, where exports reach a maximum of 27.25 billion Naira and a minimum of 7,502.5 Naira, while imports range from 27.11 billion Naira to 5,983.6 Naira. Per capita income ranges from a minimum of 199,311.3 Naira to a maximum of 379,251.6 Naira, reflecting variations in living standards over the period. The standard deviation values show significant dispersion in FDI (448 million Naira), exports (7.24 billion Naira), and imports (7.11 billion Naira), indicating wide fluctuations in these economic indicators. The skewness and kurtosis statistics suggest positive asymmetry, particularly for imports (skewness of 1.53), indicating higher values more frequently than lower ones. The Jarque-Bera test shows that the exchange rate, exports, and imports exhibit non-normal distribution, with p-values below 0.05, suggesting significant deviations from normality among the variables. Table 2 Result of Unit Root Test Variables ADF Test Phillip Peron Test Remarks Levels Difference 1st Difference Levels Difference 1st Difference InPCI **-0.92 [0.77] **-4.69 [0.00] **-1.164 [0.68] **-4.68 [0.00] I 1 InIMP **-1.08 [0.71] **-7.28 [0.00] **-1.59 [0.23] **-2.60 [0.00] I 0 InEXP **-1.28 [0.89] **-6.18 [0.00] **-0.42 [0.89] **-6.18 [0.00] I 1 InFDI ** -1.41 [0.57] **-8.60 [0.00] -2.95 0.05 7.18 [0.00] I 1 EXR **2.86 [0.01] **-4.21 [0.00] ** 3.19 [0.00] **-4.13 [0.00] I 0 Source: Author’s Computation, 2024 Table 2 shows the result of the Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test that determines whether the variables are stationary or non-stationary, which is critical for accurate time series analysis. For per capita income (InPCI), both the ADF and PP tests show non-stationarity at levels, with p-values of 0.77 and 0.68, respectively. However, the variable becomes stationary after first differencing, as indicated by the p-values of 0.00 for both tests, confirming that InPCI is integrated of order one, I(1). Imports (InIMP) exhibit non-stationarity at levels with p-values of 0.71 (ADF) and 0.23 (PP). After first differencing, the variable becomes stationary, with p-values of 0.00 for both tests, indicating that InIMP is integrated of order zero, I(0). For exports (InEXP), the ADF and PP tests show non-stationarity at levels, with p-values of 0.89 for both tests. Similar to imports, InEXP becomes stationary at the first difference with p-values of 0.00, suggesting that InEXP is integrated of order one, I(1). Foreign direct investment (InFDI) is non-stationary at levels based on the ADF test (p = 0.57) but stationary at the first difference with p-values of 0.00 for both tests, implying that InFDI is integrated of order one, I(1). Exchange rate (EXR) shows stationarity at levels in both tests, with p-values of 0.01 and 0.00 for the ADF and PP tests, respectively. This means that EXR is integrated of order zero, I(0). Table 3 Result of Lag Length Selection Criterion Lag LogL LR FPE AIC SC HQ 0 -332.3912 NA 17.51539 17.30211 22.48358 17.37864 1 -121.1375 357.5063 9.030303* 7.750640 0.001618* 8.209772* 2 -94.78927 37.83334* 10.02755 7.681501* 0.001614 8.523243 * indicates lag order selected by the criterion Source: Author’s Computation, 2024 Table 3 presents the results of the lag length selection criteria to determine the optimal lag order for model two. The results show that a lag order of 1 is most consistently recommended across several criteria (FPE, AIC, and HQ) as indicated by the asterisks. The SC criterion shows the lowest values across the other critical selection parameters, reinforcing its suitability. Therefore, a lag length of 1 is chosen for estimating the relationships between variables in this study. Table 4 Result of ARDL Bound Test Test Statistic Value K F-statistic 7.398249 4 Critical Value Bounds Significance I0 Bound I1 Bound 10% 2.45 4.52 5% 3.66 5.01 2.5% 2.51 4.56 1% 2.47 4.89 Source: Author’s Computation, 2024 The ARDL Bound Test results in Table 4 indicate that the F-statistic value is 7.398249, which is higher than the critical value I 0 bounds at the 5% significance level. This indicates that there is the existence of a long-run relationship between the variables in the model. Table 5 Result of Error Correction Model (ECM) Dependent Variable: D(LOGPCI) Variable Coefficient Std. Error t-Statistic Prob. D(LOGPCI(-1)) 0.805082 0.180587 4.458133 0.0001 D(LOGIMP(-1)) -0.017954 0.024162 0.743073 0.4627 D(LOGEXP(-1)) 0.00446 0.02127 -0.20968 0.8352 D(LOGFDI(-1)) 0.00364 0.007494 0.485694 0.6304 D(EXCHR(-1)) -0.000105 0.000315 0.331925 0.042 ECT(-1) -0.731758 0.265284 -2.758389 0.0094 C -0.000326 0.007376 -0.044218 0.965 R-squared 0.441133 Mean dependent var 0.009423 Adjusted R-squared 0.33952 S.D. dependent var 0.043226 S.E. of regression 0.03513 Akaike info criterion -3.701918 Sum squared resid 0.040725 Schwarz criterion -3.406364 Log likelihood 81.03836 Hannan-Quinn criter. -3.595055 F-statistic 4.341334 Durbin-Watson stat 1.937132 Prob(F-statistic) 0.002463 Source: Author’s Computation, 2024 Table 5 presents the results of the ECM that examines the relationship between economic growth and international trade. The Error Correction Term (ECT(-1)) has a coefficient of -0.731758 with a probability of 0.0094. This indicates a significant and strong speed of adjustment towards long-run equilibrium. The negative sign and significance imply that about 73.18% of any disequilibrium from the previous period is corrected in the current period, confirming a long-run relationship among the variables. The coefficient of D(LOGPCI(-1)) is 0.805082 with a probability value of 0.0001, which implies that a 1% increase in the one-period lagged per capita income leads to a significant rise of approximately 0.81% in the current value of per capita income. D(LOGIMP(-1)) has a coefficient of -0.017954 and a probability value of 0.4627, the result indicates that a 1% increase in imports has a negative but statistically insignificant 0.018% impact on per capita income. The coefficient of D(LOGEXP(-1)) is 0.004460 with a probability value of 0.8352, implying that a 1% increase in exports leads to an insignificant increase in per capita income by approximately 0.004%. For D(LOGFDI(-1)), the coefficient is 0.003640, and the probability value is 0.6304, this implies that a 1% rise in FDI net inflows results in an insignificant increase of about 0.004% in per capita income. The exchange rate, D(EXCHR(-1)), has a coefficient of -0.000105 and a probability value of 0.0420, indicating that a 1% increase in the exchange rate significantly decreases per capita income by about 0.01%. The R-squared value is 0.441133, indicating that 44.1% of the variation in per capita income is explained by the independent variables in the model. The F-statistic of 4.341334 with a probability of 0.002463 confirms the overall statistical significance of the model at the 5% level. The Durbin-Watson statistic of 1.937132, which is close to 2, suggests no significant issues of autocorrelation in the model. Table 6 Result Heteroskedasticity and Serial Autocorrelation LM Test Result Test F-Statistics Probability Remarks Autocorrelation 0.91 0.49 No Autocorrelation Heteroskedasticity 0.23 0.64 No Heteroskedasticity Source: Author’s Computation, 2024 The autocorrelation test in Table 6 shows an F-statistic of 0.91 and a p-value of 0.49. With the p-value being above 0.05, the null hypothesis of no autocorrelation is accepted. The heteroskedasticity test also results also depict an F-statistic of 0.23 and a p-value of 0.64, indicating no significant evidence of heteroskedasticity and supporting the model’s assumption of homoskedasticity. The results indicate no significant issues with serial autocorrelation or heteroskedasticity across in the model, as all p-values exceed the 0.05 significance level. This implies that the residuals of the models are well-behaved, satisfying the key assumptions of the ECM and reinforcing the reliability of the models for inference. The residual normality test presented in Fig. 1 above shows the mean of the residuals is approximately zero (-5.42e-19), and the median (-0.004017) is also close to zero, suggesting that the residuals are symmetrically distributed around zero. The maximum and minimum values indicate a moderate spread of the residuals, with a standard deviation of 0.032315, showing that the residuals are closely clustered around the mean. The skewness value of 0.401733 indicates a slight right skew, while the kurtosis of 3.439052, which is near 3, suggests that the distribution has a peak and tails similar to a normal distribution. The Jarque-Bera statistic, with a probability of 0.497279 (greater than 0.05), implies that the residuals are normally distributed. These results confirm that the residuals are approximately normal, supporting the model's assumptions and enhancing the credibility of ECM regression results. The CUSUM (Cumulative Sum) test presented in the graph assesses the stability of the model's coefficients over time. The fluctuating line in the middle represents the cumulative sum of the residuals, while the two red lines going in opposite directions indicate the 5% significance bounds. Throughout the period analyzed, the CUSUM line remains well within these significance bounds, suggesting that there are no significant structural breaks in the model. This stability indicates that the estimated coefficients are consistent over time, reinforcing the reliability of the model for inference and prediction. The absence of points crossing the significance bounds confirms the model's robustness and stability throughout the sample period. Discussion and Conclusion The study investigated the relationship between international trade and economic growth in Nigeria from 1981 to 2023. The results of the stationarity test revealed that the variables in the models were of mixed stationarity at I 1 and I 0 . The ARDL Bound test showed that models exhibited evidence of long-run equilibrium, as their F-statistics surpassed the critical bounds across various significance levels. The Error Correction Term (ECT(-1)) has a coefficient of -0.731758 with a probability value of 0.0094, indicating a significant speed of adjustment towards long-run equilibrium. This finding shows that approximately 73.18% of any disequilibrium is corrected in each period, confirming the existence of a long-term relationship among the variables. This result aligns with the findings of Shido-Ikwu et al. ( 2023 ), who also discovered a long-run relationship between trade variables and Nigeria’s economic growth using the ARDL bounds test. Both studies confirm that the Nigerian economy adjusts to long-run equilibrium following trade-related shocks, underscoring the resilience of the economy in responding to international trade fluctuations. The significant speed of adjustment indicates that Nigeria’s economy is capable of correcting short-term imbalances caused by trade dynamics. From the findings, total import D(LOGIMP(-1)) with a coefficient of -0.017954 with a probability value of 0.4627, indicating that a 1% increase in imports has a negative but statistically insignificant impact on per capita income. This finding is consistent with the results of Omoke and Opuala–Charles ( 2021 ), who found that imports had a significant negative effect on Nigeria’s economic growth in the long term, particularly when institutional quality was low. Similarly, Yusuff et al. ( 2020 ) reported a negative connection between foreign trade (imports) and GDP per capita during their study period, suggesting that Nigeria’s reliance on imports may not be beneficial for economic growth. The negative but insignificant impact of imports reinforces the notion that Nigeria’s import structure, which relies heavily on consumer goods rather than productive capital goods, may not support economic growth. This finding implies that policymakers should focus on improving the quality and composition of imports by encouraging the importation of goods that enhance production capacity and technological advancement. Total export D(LOGEXP(-1)) with a coefficient 0.004460 and a probability value of 0.8352, indicating a positive but insignificant effect on per capita income. This result contrasts with the findings of Shido-Ikwu et al. ( 2023 ), who reported a direct and significant impact of export trade on Nigeria’s economic growth. In contrast, Sun and Heshmati (2010) observed that international trade, particularly high-tech exports, had a significant and positive effect on China’s regional productivity and economic growth, with the eastern region benefiting the most from export-driven growth. The insignificant impact of exports in this study suggests that Nigeria’s export structure, dominated by crude oil, may not be sufficient to drive significant growth. Unlike China’s diversified export structure, Nigeria’s reliance on a single commodity limits the potential benefits of trade. The coefficient of FDI D(LOGFDI(-1)) of 0.003640 with a probability value of 0.6304, indicates an insignificant impact on per capita income. This finding is consistent with the results of Shido-Ikwu et al. ( 2023 ), who also found that FDI had an insignificant impact on Nigeria’s economic growth. This may be due to the concentration of FDI in sectors like oil and gas, which have limited linkages with the rest of the economy. Similarly, Yusuff et al. ( 2020 ) found that foreign trade and FDI did not positively contribute to Nigeria’s economic growth, suggesting that FDI inflows may not always stimulate significant growth in developing economies with weak institutions. The insignificant influence of FDI on per capita income implies that while FDI inflows can be beneficial, their impact may be limited if they are concentrated in sectors with weak linkages to the broader economy. Exchange rate, D(EXCHR(-1)), has a coefficient of -0.000105 and a probability value of 0.0420, indicating that an increase in the exchange rate (depreciation) significantly reduces per capita income. This result supports the findings of Shido-Ikwu et al. ( 2023 ), who reported that exchange rate fluctuations had a negative and insignificant impact on Nigeria’s economic growth. Similarly, Emehelu ( 2021 ) found that exchange rate depreciation negatively impacted economic growth, highlighting the vulnerability of developing economies to currency fluctuations. The result implies that Nigeria’s reliance on imports, combined with a weakening currency, leads to higher inflation and reduced purchasing power, ultimately hindering economic growth. This finding suggests that exchange rate fluctuations in the exchange rate can have detrimental effects on economic performance. Conclusion The findings of this study reveal that international trade has an insignificant impact on Nigeria's economic growth over the study period. While exports show a positive relationship with per capita income, their impact remains statistically insignificant, implying that Nigeria’s export structure, heavily reliant on crude oil, does not fully contribute to growth. Similarly, imports exhibit a negative but insignificant effect on per capita income, reinforcing concerns that the composition of imports, primarily consumer goods, does not stimulate domestic production or economic expansion. Recommendations For international trade to have a significant and positive effect on economic growth in Nigeria, the following strategic policy recommendations should be implemented to enhance trade and investment frameworks, as well as stabilizing macroeconomic fundamentals such as the exchange rate: The government should focus on promoting non-oil sectors such as agriculture, manufacturing, and technology by providing incentives and support for producers to engage in international markets. This will create a more resilient economy less vulnerable to global oil price fluctuations. The government should revise import policies to prioritize the importation of capital goods and machinery that enhance local production to strengthen its industrial base and reduce reliance on consumer goods imports that do not contribute meaningfully to growth. The government should implement policies that will stabilize the exchange rate and reduce exchange rate volatility through managing foreign reserves effectively and supporting local industries to reduce import dependency. The government should implement trade policy reforms that simplify procedures and reduce trade barriers to expand market access, foster competition, and increase Nigeria’s integration into global supply chains. Declarations Ethical Approval and Consent to Participate: This study did not involve human participants, animals, or sensitive data requiring ethical approval. As such, no ethical approval was necessary. However, the research adhered to all applicable institutional guidelines and best practices. Consent for Publication: The manuscript does not contain any individual person’s data in any form (including individual details, images, or videos) that would require consent to publish. Therefore, consent for publication is not applicable in this case. Funding: Not applicable References Abebefe, H. A. (1995). The structure of Nigeria’s external trade. Central Bank of Nigeria Bullion , 19(4), October/December. Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(5) https://doi.org/10.1257/aer.91.5.1369 , 1369–1401. Adeleye, J. O., Adeteye, O. S., & Adewuyi, M. O. (2015). Impact of international trade on economic growth in Nigeria. International Journal of Financial Research (IJFR), 6(3) , 23-30. Afolabi, B., Danladi, J. D., & Azeez, M. I. (2017). International trade and economic growth in Nigeria. . Global Journal of Human-Social Science: Economics, 17(5) , 1-12. Appleyard, D. R., & Field, A. J. (1998). International economics: Trade theory and policy (3rd ed.). Irwin/McGraw-Hill. Arodoye, N. L., & Iyoha, M. A. (2014). Foreign trade-economic growth nexus: Evidence from Nigeria. CBN Journal of Applied Statistics, 5(1) , 23-31. Collier, P. (2007). The bottom billion. ECONOMIC REVIEW-DEDDINGTON 25(1) , 17-19. De Matteis, A. (2004). International trade and economic growth in a global environment. Journal of International Development, 16(4) , 575-588. Emehelu, C. I. (2021). Effects of international trade on the economic growth of Nigeria. International Journal of Innovative Finance and Economics Research, 9(1) , 144-157. Ezindu, O. N., Nkechi, O. J., Victoria, O. I., & Chike, U. R. (2020). Impact of international trade on Nigerian economic growth: Evidence from oil terms of trade. International Journal of Economics and Financial Management, 5(2) , 31-47. Heckscher, E. F., & Ohlin, B. (1991). Heckscher-Ohlin trade theory. MIT: H. Flam & M. J. Flanders, Eds. Ingram, J. C., & Dunn, R. M. (1993). International economics. John Wiley and Sons Inc. InternationalMonertaryFund. (2020). IMF Annual Report 2020: Supporting a resilient recovery. IMF. https://www.imf.org/en/Publications. Keho, Y., & Wang, M. (2017). The impact of trade openness on economic growth: The case of Cote d’Ivoire. Cogent Economics & Finance, 5(1) , 1-14. Krugman, P., & Obstfeld, M. (2020). International economics: Theory and policy (11th ed.). Pearson. Lin, J. Y. (2001). Demystifying the Chinese economy. Cambridge University Press. Lipsey, R. G. (1986). Successes and failures in the transformation of economics. Journal of Economic Methodology, 8(2) , 169-201. Mannur, H. G. (1995). The principle of trading II. International Economics. Second Revised Edition. New Delhi, India . Mike, I. O., & Okojie, I. E. (2012). An empirical analysis of the impact of trade on economic growth in Nigeria. Journal of Developing Societies, 5(6) , 77-82. Nordhaus, W. D. (2002). The health of nations: the contribution of improved health to living standards (Vol. 8818). Cambridge: National Bureau of Economic Research. Omoke, P. C., & Opuala–Charles, S. (2021). Trade openness and economic growth nexus: Exploring the role of institutional quality in Nigeria. Cogent Economics & Finance, 9(1) , 1-17. Ricardo, D. (1821). On the principles of political economy. London: J. Murray. Rodrik, D. (2011). The globalization paradox: why global markets, states, and democracy can't coexist. Oxford University Press. Rodrik, D. (2018). Straight talk on trade: Ideas for a sane world economy. Princeton University Press. Shido-Ikwu, S. B., Dankumo, A. M., Pius, F. M., & Fazing, E. Y. (2023). Impact of international trade on economic growth in Nigeria. Lafia Journal of Economics and Management Sciences, 8 , 212-226. Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. London: W. Strahan and T. Cadell. Stiglitz, J. E. (2002). Information and the Change in the Paradigm in Economics. . American economic review, 92(3) , 460-501. Sun, P. &. (2010). International trade and its effects on economic growth in China. World-Trade-Organization. (2018). The future of world trade: How digital technologies are transforming global commerce. Geneva: WTO Publications https://www.wto.org. Yusuff, S., Adekanye, T., & Babalola, O. A. (2020). International trade and its effect on economic growth in Nigeria (1986-2017). American Journal of Economics, 4(2) , 70-85. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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The classical theories of(Smith, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1776\u003c/span\u003e) and(Ricardo, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1821\u003c/span\u003e) emphasized the benefits of specialization and comparative advantage, both of which remain relevant in today\u0026rsquo;s globalized economy. Globalization has further amplified the importance of trade, spreading technology, fostering competition, and promoting industrial specialization, thereby stimulating economic development (Heckscher \u0026amp; Ohlin, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). These theoretical frameworks highlight how trade can promote growth, but its effects vary depending on country-specific factors like institutional frameworks, infrastructure, and industrial policies (Rodrik, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn nations like China and India, trade has contributed significantly to rapid economic growth, helping them transition from agrarian-based economies to major global players, thus lifting millions out of poverty (Lin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). However, the same cannot be said for many sub-Saharan African countries, including Nigeria, which despite participating in global trade, have not seen comparable levels of economic growth. This raises concerns about whether trade alone can serve as a reliable tool for development in such regions (Collier, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In Nigeria, international trade, particularly in the oil and agricultural sectors, has played a significant role in generating revenue. Before the discovery of oil, Nigeria relied heavily on agricultural exports such as palm oil, groundnuts, and cocoa. However, the focus has since shifted to oil, contributing to a decline in agricultural productivity and a growing dependency on crude oil exports (Ezindu, Nkechi, Victoria, \u0026amp; Chike, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its potential to spur growth, Nigeria\u0026rsquo;s overreliance on crude oil and the high volume of imports, particularly of machinery, equipment, and food products, has left the country vulnerable to global market fluctuations. Trade liberalization, widely promoted by the World Trade Organization (WTO) and the International Monetary Fund (IMF), is believed to reduce barriers and boost productivity (International Monetary Fund, 2020; World Trade Organization, 2018). However, for countries like Nigeria, where weak institutions, erratic exchange rates, and infrastructure deficits persist, the anticipated benefits of liberalized trade have been inconsistent (Stiglitz, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This study aims to analyze the impact of international trade on Nigeria's economic growth while exploring factors such as political stability, institutional quality, and infrastructure that may shape the trade-growth relationship. (Acemoglu, Johnson, \u0026amp; Robinson) suggest that countries with strong institutions are better positioned to harness the benefits of international trade by integrating into global value chains. Conversely, Rodrik (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) argues that countries with weak institutions and inadequate infrastructure may struggle to compete globally, thereby missing out on the benefits of trade liberalization.\u003c/p\u003e \u003cp\u003eThe analysis in this study focuses solely on Nigeria, using secondary data on economic growth proxy for GDP per capita income and international trade proxy with total imports and exports covering the period from 1981 to 2022. To obtain robust estimation results, the study will utilize relevant econometric methodologies that account for the complexities of time-series data. This study reviews existing literature on international trade and growth, draws on both theoretical and empirical contributions, and presents a detailed analysis of the data and methodology. The findings will offer recommendations for strengthening Nigeria\u0026rsquo;s position in global trade, fostering long-term economic development, and reducing its reliance on volatile sectors.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConceptual Review\u003c/h2\u003e \u003cp\u003eInternational Trade\u003c/p\u003e \u003cp\u003eAbebefe (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) stated that trade involves multiple exchanges of products carried out through market interactions. Transactions occurring beyond the jurisdiction of a sovereign state are considered international. Similarly, Nordhaus (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) argued that the system in which countries import and export products, capital, and services is referred to as international trade. They differentiate between domestic and global trade based on increased trade possibilities, sovereign nations, and exchange rates, highlighting their practical and economic significance. Foreign trade is driven by the encouragement of specialization, leading to increased production (Ingram \u0026amp; Dunn, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Nordhaus, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Mannur (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) posited that international trade involves the exchange of products and services among citizens of different countries, serving as a means to facilitate global service flows, trade in goods, and factor movements. It is based on the understanding that no single nation can provide all the goods and services its population requires due to resource limitations and disparities. Similarly, Classical and neo-classical economists viewed foreign trade as an integral to a country's development process and as a source of growth. With globalization and international trade, nations have become increasingly interconnected in recent years. Afolabi, Danladi, and Azeez (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) noted that foreign trade is the most significant and enduring aspect of a country's international economic relations.\u003c/p\u003e \u003cp\u003eConcepts of Growth\u003c/p\u003e \u003cp\u003eLipsey (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) defined growth as a long-term upward trend in a nation's total output. This indicates a sustained increase in Gross Domestic Product (GDP) over an extended period. GDP is a measure of an economy's total output of goods and services, is commonly used to describe economic growth. In other to represent the real value of an economy, GDP is adjusted for inflation to gauge economic growth accurately. This adjustment, known as rebasing, was carried out by Nigeria in 2015 to account for inflation's effects and provide precise measures of growth over time. Economic growth is quantified by increases in the quantity of goods and services over time.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEmpirical Review\u003c/h3\u003e\n\u003cp\u003eSeveral efforts have been made to investigate the connection between global trade and economic growth empirically, and the findings of these studies have been inconsistent. For instance, Shido-Ikwu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigate the impact of international trade on Nigeria's economic growth from 1981 to 2019, utilizing the Autoregressive Distributive Lag (ARDL) approach. They discovered a long-run relationship between the variables through the ARDL bound test approach, the short-run and long-run estimations indicate that import trade, foreign direct investment (FDI), and the exchange rate have a negative and insignificant impact on Nigeria's economic growth. However, export trade shows a direct and significant impact on economic growth during the study period. The study concludes that international trade had an insignificant impact on Nigeria's economic growth over the period under review.\u003c/p\u003e \u003cp\u003eSimilarly, Sun and Heshmati (2010) explore the role of international trade in China's economic growth, highlighting its increasingly significant contribution. The study begins by reviewing the concepts and evolution of China's international trade regime, as well as the policies favoring trade sectors. The research extensively analyzes China's international trade performance and evaluates its effects on economic growth, focusing on productivity improvement. Both econometric and non-parametric approaches are employed using a 6-year balanced panel data of 31 Chinese provinces from 2002 to 2007. In the econometric approach, a stochastic frontier production function is estimated to identify province-specific determinants of inefficiency in trade. Meanwhile, the non-parametric approach calculates the Divisia index for each province/region to serve as a benchmark.\u003c/p\u003e \u003cp\u003eThe study concludes that increased participation in global trade has enabled China to realize both static and dynamic benefits, leading to rapid national economic growth. Both the volume of international trade and the trade structure, particularly towards high-tech exports, have positive effects on China's regional productivity. However, the eastern region of China has seen the most rapid development, while the central and western provinces have lagged behind in both economic growth and international trade participation.\u003c/p\u003e \u003cp\u003eEmehelu (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted an empirical analysis of the impact of international trade on Nigeria's economic growth over the period 1981\u0026ndash;2018, employing the Ordinary Least Squares (OLS) technique. The research investigated the effects of exchange rates, trade policy changes, and the influence of trade liberalization on Nigeria's economic growth. The study employed independent variables such as policy changes (dummy), exchange rates, and liberalization/openness, regressing them on the real Gross Domestic Product (GDP) of Nigeria using secondary data from the Central Bank of Nigeria Statistical Bulletin 2018.\u003c/p\u003e \u003cp\u003eEconometric diagnostics were conducted to check for unit roots in the series using the Augmented Dickey-Fuller technique. The tests indicated that the variables were integrated at order 1(1). The Johansen co-integration test was also performed to assess the long-run relationship among the variables, confirming the absence of long-run equilibrium. The study's findings revealed a negative and insignificant relationship between exchange rates and economic growth in Nigeria. Moreover, various trade policies in Nigeria were found to negatively and significantly impact GDP growth, hindering economic prosperity. As a result, the study recommends that, given the limited significant effects of import and export trade on Nigeria's growth, the federal government should implement programs and policies that promote local production while discouraging the importation of specific essential products.\u003c/p\u003e \u003cp\u003eSimilarly, Omoke and Opuala\u0026ndash;Charles (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) considered institutional quality and investigated the relationship between trade openness and Nigeria's economic growth. They utilized indices of trade openness, such as total trade, import trade, and export trade, spanning from 1984 to 2017. The study determined the long-run relationship using the ARDL bounds testing technique and discovered that import trade had a significant negative influence on economic growth, while export trade had a significant positive impact on economic growth, aligning with prior expectations. Additionally, the results indicated a negative impact of import trade on economic growth in the long-term when institutional quality in Nigeria was less pronounced. The study concluded that the benefits of trade can be channeled towards initiatives promoting economic growth, particularly with the support of strong institutions and good governance.\u003c/p\u003e \u003cp\u003eLikewise, Yusuff, Adekanye, and Babalola (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigated how foreign trade affects the expansion of the Nigerian economy from 1986 to 2017 using Ordinary Least Squares (OLS) estimation techniques. Their results showed a negative connection between foreign trade and GDP per capita during the study period. The study recommended that the government implement crucial trade-oriented policies to stimulate economic growth through increased exports and accumulation of more foreign revenues to drive production growth in the nation.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical framework\u003c/h2\u003e \u003cp\u003eThe theoretical framework of this study is anchored on the comparative advantage theory propounded by David Ricardo in 1817. According to the theory, countries should specialize in producing goods and services where they have a lower opportunity cost than other nations, enabling them to efficiently allocate their resource, leading to increased productivity and output. As countries trade based on their comparative advantages and focus on what can optimize their production and stimulate innovations, they can access a wider range of goods and services at lower costs, fostering economic growth.\u003c/p\u003e \u003cp\u003eModel specification\u003c/p\u003e \u003cp\u003eIn other to achieve the objective of this study. The research establishes a functional relationship between international trade and economic growth as stated implicitly below:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Economic\\:growth=f(\\:international\\:trade\\)\u003c/span\u003e \u003c/span\u003e) (1)\u003c/p\u003e \u003cp\u003eThe study proxies economic growth with GDP per capita as the dependent variable while international trade is decomposed into total import, total export, and real exchange rate and FDI as a control variable due to their significant impact on economic growth through its influence on export competitiveness, import prices, and FDI role in enhancing capital flow and technology transfer that boost economic growth.\u003c/p\u003e \u003cp\u003eThe explicit form of \u003cem\u003eEq.\u0026nbsp;1\u003c/em\u003e is specified below:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{C}\\text{I}=f(Import,\\:Export,\\:Exchange\\:rate,\\:FDI)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eeconometrically\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{PCI}_{t}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{IMP}_{t}+{\\alpha\\:}_{2}{EXP}_{t}+{\\alpha\\:}_{3}{EXR}_{t}+{\\alpha\\:}_{4}{\\text{F}\\text{D}\\text{I}}_{t\\:}+\\:{\\text{ϵ}}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTaking the log of Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e we have:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{InPCI}_{t}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{InIMP}_{t}+{\\alpha\\:}_{2}In{EXP}_{t}+{\\alpha\\:}_{3}{EXR}_{t}+{\\alpha\\:}_{4}{\\text{I}\\text{n}\\text{F}\\text{D}\\text{I}}_{t\\:}+{\\text{ϵ}}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePCI \u0026ndash; Per Capita Income in thousands of Naira\u003c/p\u003e \u003cp\u003eIMP- total import in millions of Naira\u003c/p\u003e \u003cp\u003eEXP- total export in millions of Naira\u003c/p\u003e \u003cp\u003eEXR- real exchange rate in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\raisebox{1ex}{$\\text{N}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$\\text{\\$}$}\\right.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{0}\\:\\)\u003c/span\u003e \u003c/span\u003eintercept; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1-4}\\)\u003c/span\u003e\u003c/span\u003e coefficients to be estimated\u003c/p\u003e \u003cp\u003eIn- natural logarithm; t 1981\u0026ndash;2023\u003c/p\u003e \u003cp\u003eε- error term\u003c/p\u003e \u003cp\u003eSource of data\u003c/p\u003e \u003cp\u003eSecondary data on PCI, real exchange rate, and FDI from 1981 to 2023 is sourced from the World Development Indicator, while total imports and exports were sourced from the Central Bank of Nigeria Statistical Bulletin.\u003c/p\u003e \u003cp\u003eMethod of data analysis\u003c/p\u003e \u003cp\u003eDescriptive statistics such as mean, mode, and median are adopted to determine the behavior of the data as well as the Jarque-Bera, and skewness statistics to ascertain the normal distribution. Due to the unpredictable movement of time series data, the study employ the Augmented Dickey-Fuller (ADF) and Philip Perron stationary test to determine the level of stationarity of the data. The study determines the appropriate lag length selection criterion in other to ascertain the appropriate lag period for the analysis. The investigation extends to determining the long-run relationship among variables through the Autoregressive Distributed Lag (ARDL) bound test. The study employed the Error Correction Model (ECM). To ensure robustness, the study conducts the tests for heteroscedasticity and serial autocorrelation to scrutinize potential issues in the residuals of the regression in three models. The ECM is specified below:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:{InPCI}_{t}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{\\varDelta\\:InPCI}_{t-1}+{\\alpha\\:}_{2}{\\varDelta\\:InIMP}_{t}+{\\alpha\\:}_{3}\\varDelta\\:In{EXP}_{t}+{\\alpha\\:}_{4}{\\varDelta\\:EXR}_{t}+{\\alpha\\:}_{5}\\varDelta\\:{InFDI}_{t\\:}+{\\varphi\\:}_{1}\\varDelta\\:{ECT}_{t-1}+{ϵ}_{t2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the coefficient of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\upvarphi\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e, is expected to be negative and statistically significant to confirm the presence of a long-run equilibrium relationship. While \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\alpha\\:}_{1-5}\\)\u003c/span\u003e\u003c/span\u003eare short-run coefficients that capture the immediate impact of changes in international trade on economic growth within the period under study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData analysis and Presentation of Findings\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of Descriptive Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEXR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEXP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6227897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5194325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270179.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1906839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1435438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245112.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27251572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27115109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379251.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7502.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5983.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199311.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7243131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7108949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65607.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.784806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.528542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.213611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.523062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJarque-Bera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4861.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58E\u0026thinsp;+\u0026thinsp;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11347551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum Sq. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e581975.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21E\u0026thinsp;+\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07E\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: Author\u0026rsquo;s Computation, 2024\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics for the exchange rate (EXR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\raisebox{1ex}{$\\text{N}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$\\text{\\$}$}\\right.\\)\u003c/span\u003e\u003c/span\u003e), foreign direct investment (FDI in Naira), exports (EXP in Naira), imports (IMP in Naira), and per capita income (PCI in Naira). The mean exchange rate (EXR) is 115.74 Naira, reflecting the average value of the Naira over the period. Foreign direct investment (FDI) averages 376\u0026nbsp;million Naira, while exports and imports are, on average, 6.23\u0026nbsp;billion Naira and 5.19\u0026nbsp;billion Naira, respectively. Per capita income (PCI) has a mean of 270,179.8 Naira, suggesting significant economic variation among the population.\u003c/p\u003e\n \u003cp\u003eThe median values further highlight this variability, with the exchange rate at 15.26 Naira, FDI at 125\u0026nbsp;million Naira, and exports and imports at 1.91\u0026nbsp;billion Naira and 1.44\u0026nbsp;billion Naira, respectively. The maximum and minimum values show extreme fluctuations, particularly in exports and imports, where exports reach a maximum of 27.25\u0026nbsp;billion Naira and a minimum of 7,502.5 Naira, while imports range from 27.11\u0026nbsp;billion Naira to 5,983.6 Naira. Per capita income ranges from a minimum of 199,311.3 Naira to a maximum of 379,251.6 Naira, reflecting variations in living standards over the period.\u003c/p\u003e\n \u003cp\u003eThe standard deviation values show significant dispersion in FDI (448 million Naira), exports (7.24 billion Naira), and imports (7.11 billion Naira), indicating wide fluctuations in these economic indicators. The skewness and kurtosis statistics suggest positive asymmetry, particularly for imports (skewness of 1.53), indicating higher values more frequently than lower ones. The Jarque-Bera test shows that the exchange rate, exports, and imports exhibit non-normal distribution, with p-values below 0.05, suggesting significant deviations from normality among the variables.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of Unit Root Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eADF Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePhillip Peron Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRemarks\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevels Difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1st Difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevels Difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1st Difference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInPCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-0.92\u003c/p\u003e\n \u003cp\u003e[0.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-4.69\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-1.164\u003c/p\u003e\n \u003cp\u003e[0.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-4.68\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInIMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-1.08\u003c/p\u003e\n \u003cp\u003e[0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-7.28\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-1.59\u003c/p\u003e\n \u003cp\u003e[0.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-2.60\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInEXP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-1.28\u003c/p\u003e\n \u003cp\u003e[0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-6.18\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-0.42\u003c/p\u003e\n \u003cp\u003e[0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-6.18\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e** -1.41\u003c/p\u003e\n \u003cp\u003e[0.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-8.60\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.95\u003c/p\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.18\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEXR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**2.86\u003c/p\u003e\n \u003cp\u003e[0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-4.21\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e** 3.19\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**-4.13\u003c/p\u003e\n \u003cp\u003e[0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: Author\u0026rsquo;s Computation, 2024\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the result of the Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test that determines whether the variables are stationary or non-stationary, which is critical for accurate time series analysis. For per capita income (InPCI), both the ADF and PP tests show non-stationarity at levels, with p-values of 0.77 and 0.68, respectively. However, the variable becomes stationary after first differencing, as indicated by the p-values of 0.00 for both tests, confirming that InPCI is integrated of order one, I(1).\u003c/p\u003e\n \u003cp\u003eImports (InIMP) exhibit non-stationarity at levels with p-values of 0.71 (ADF) and 0.23 (PP). After first differencing, the variable becomes stationary, with p-values of 0.00 for both tests, indicating that InIMP is integrated of order zero, I(0). For exports (InEXP), the ADF and PP tests show non-stationarity at levels, with p-values of 0.89 for both tests. Similar to imports, InEXP becomes stationary at the first difference with p-values of 0.00, suggesting that InEXP is integrated of order one, I(1). Foreign direct investment (InFDI) is non-stationary at levels based on the ADF test (p\u0026thinsp;=\u0026thinsp;0.57) but stationary at the first difference with p-values of 0.00 for both tests, implying that InFDI is integrated of order one, I(1). Exchange rate (EXR) shows stationarity at levels in both tests, with p-values of 0.01 and 0.00 for the ADF and PP tests, respectively. This means that EXR is integrated of order zero, I(0).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of Lag Length Selection Criterion\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLag\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHQ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-332.3912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.51539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.30211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.48358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.37864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-121.1375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e357.5063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.030303*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.750640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001618*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.209772*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-94.78927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.83334*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.02755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.681501*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.523243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e* indicates lag order selected by the criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eSource: Author\u0026rsquo;s Computation, 2024\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the lag length selection criteria to determine the optimal lag order for model two. The results show that a lag order of 1 is most consistently recommended across several criteria (FPE, AIC, and HQ) as indicated by the asterisks. The SC criterion shows the lowest values across the other critical selection parameters, reinforcing its suitability. Therefore, a lag length of 1 is chosen for estimating the relationships between variables in this study.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of ARDL Bound Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.398249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical Value Bounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI0 Bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI1 Bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eSource: Author\u0026rsquo;s Computation, 2024\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe ARDL Bound Test results in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that the F-statistic value is 7.398249, which is higher than the critical value I\u003csub\u003e0\u003c/sub\u003e bounds at the 5% significance level. This indicates that there is the existence of a long-run relationship between the variables in the model.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of Error Correction Model (ECM)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eDependent Variable: D(LOGPCI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LOGPCI(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.458133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LOGIMP(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LOGEXP(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LOGFDI(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.485694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(EXCHR(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.331925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECT(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.731758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.758389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.044218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.441133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eS.D. dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS.E. of regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAkaike info criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.701918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum squared resid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSchwarz criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.406364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.03836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHannan-Quinn criter.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.595055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.341334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDurbin-Watson stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.937132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb(F-statistic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s Computation, 2024\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the ECM that examines the relationship between economic growth and international trade. The Error Correction Term (ECT(-1)) has a coefficient of -0.731758 with a probability of 0.0094. This indicates a significant and strong speed of adjustment towards long-run equilibrium. The negative sign and significance imply that about 73.18% of any disequilibrium from the previous period is corrected in the current period, confirming a long-run relationship among the variables.\u003c/p\u003e\n \u003cp\u003eThe coefficient of D(LOGPCI(-1)) is 0.805082 with a probability value of 0.0001, which implies that a 1% increase in the one-period lagged per capita income leads to a significant rise of approximately 0.81% in the current value of per capita income. D(LOGIMP(-1)) has a coefficient of -0.017954 and a probability value of 0.4627, the result indicates that a 1% increase in imports has a negative but statistically insignificant 0.018% impact on per capita income. The coefficient of D(LOGEXP(-1)) is 0.004460 with a probability value of 0.8352, implying that a 1% increase in exports leads to an insignificant increase in per capita income by approximately 0.004%.\u003c/p\u003e\n \u003cp\u003eFor D(LOGFDI(-1)), the coefficient is 0.003640, and the probability value is 0.6304, this implies that a 1% rise in FDI net inflows results in an insignificant increase of about 0.004% in per capita income. The exchange rate, D(EXCHR(-1)), has a coefficient of -0.000105 and a probability value of 0.0420, indicating that a 1% increase in the exchange rate significantly decreases per capita income by about 0.01%. The R-squared value is 0.441133, indicating that 44.1% of the variation in per capita income is explained by the independent variables in the model. The F-statistic of 4.341334 with a probability of 0.002463 confirms the overall statistical significance of the model at the 5% level. The Durbin-Watson statistic of 1.937132, which is close to 2, suggests no significant issues of autocorrelation in the model.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult Heteroskedasticity and Serial Autocorrelation LM Test Result\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-Statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRemarks\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutocorrelation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Autocorrelation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeteroskedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Heteroskedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eSource: Author\u0026rsquo;s Computation, 2024\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe autocorrelation test in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows an F-statistic of 0.91 and a p-value of 0.49. With the p-value being above 0.05, the null hypothesis of no autocorrelation is accepted. The heteroskedasticity test also results also depict an F-statistic of 0.23 and a p-value of 0.64, indicating no significant evidence of heteroskedasticity and supporting the model\u0026rsquo;s assumption of homoskedasticity. The results indicate no significant issues with serial autocorrelation or heteroskedasticity across in the model, as all p-values exceed the 0.05 significance level. This implies that the residuals of the models are well-behaved, satisfying the key assumptions of the ECM and reinforcing the reliability of the models for inference.\u003c/p\u003e\n \u003cp\u003eThe residual normality test presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e above shows the mean of the residuals is approximately zero (-5.42e-19), and the median (-0.004017) is also close to zero, suggesting that the residuals are symmetrically distributed around zero. The maximum and minimum values indicate a moderate spread of the residuals, with a standard deviation of 0.032315, showing that the residuals are closely clustered around the mean. The skewness value of 0.401733 indicates a slight right skew, while the kurtosis of 3.439052, which is near 3, suggests that the distribution has a peak and tails similar to a normal distribution. The Jarque-Bera statistic, with a probability of 0.497279 (greater than 0.05), implies that the residuals are normally distributed. These results confirm that the residuals are approximately normal, supporting the model\u0026apos;s assumptions and enhancing the credibility of ECM regression results.\u003c/p\u003e\n \u003cp\u003eThe CUSUM (Cumulative Sum) test presented in the graph assesses the stability of the model\u0026apos;s coefficients over time. The fluctuating line in the middle represents the cumulative sum of the residuals, while the two red lines going in opposite directions indicate the 5% significance bounds. Throughout the period analyzed, the CUSUM line remains well within these significance bounds, suggesting that there are no significant structural breaks in the model. This stability indicates that the estimated coefficients are consistent over time, reinforcing the reliability of the model for inference and prediction. The absence of points crossing the significance bounds confirms the model\u0026apos;s robustness and stability throughout the sample period.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThe study investigated the relationship between international trade and economic growth in Nigeria from 1981 to 2023. The results of the stationarity test revealed that the variables in the models were of mixed stationarity at I\u003csub\u003e1\u003c/sub\u003e and I\u003csub\u003e0\u003c/sub\u003e. The ARDL Bound test showed that models exhibited evidence of long-run equilibrium, as their F-statistics surpassed the critical bounds across various significance levels. The Error Correction Term (ECT(-1)) has a coefficient of -0.731758 with a probability value of 0.0094, indicating a significant speed of adjustment towards long-run equilibrium. This finding shows that approximately 73.18% of any disequilibrium is corrected in each period, confirming the existence of a long-term relationship among the variables. This result aligns with the findings of Shido-Ikwu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who also discovered a long-run relationship between trade variables and Nigeria\u0026rsquo;s economic growth using the ARDL bounds test. Both studies confirm that the Nigerian economy adjusts to long-run equilibrium following trade-related shocks, underscoring the resilience of the economy in responding to international trade fluctuations. The significant speed of adjustment indicates that Nigeria\u0026rsquo;s economy is capable of correcting short-term imbalances caused by trade dynamics.\u003c/p\u003e \u003cp\u003eFrom the findings, total import D(LOGIMP(-1)) with a coefficient of -0.017954 with a probability value of 0.4627, indicating that a 1% increase in imports has a negative but statistically insignificant impact on per capita income. This finding is consistent with the results of Omoke and Opuala\u0026ndash;Charles (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found that imports had a significant negative effect on Nigeria\u0026rsquo;s economic growth in the long term, particularly when institutional quality was low. Similarly, Yusuff et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported a negative connection between foreign trade (imports) and GDP per capita during their study period, suggesting that Nigeria\u0026rsquo;s reliance on imports may not be beneficial for economic growth. The negative but insignificant impact of imports reinforces the notion that Nigeria\u0026rsquo;s import structure, which relies heavily on consumer goods rather than productive capital goods, may not support economic growth. This finding implies that policymakers should focus on improving the quality and composition of imports by encouraging the importation of goods that enhance production capacity and technological advancement.\u003c/p\u003e \u003cp\u003eTotal export D(LOGEXP(-1)) with a coefficient 0.004460 and a probability value of 0.8352, indicating a positive but insignificant effect on per capita income. This result contrasts with the findings of Shido-Ikwu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who reported a direct and significant impact of export trade on Nigeria\u0026rsquo;s economic growth. In contrast, Sun and Heshmati (2010) observed that international trade, particularly high-tech exports, had a significant and positive effect on China\u0026rsquo;s regional productivity and economic growth, with the eastern region benefiting the most from export-driven growth. The insignificant impact of exports in this study suggests that Nigeria\u0026rsquo;s export structure, dominated by crude oil, may not be sufficient to drive significant growth. Unlike China\u0026rsquo;s diversified export structure, Nigeria\u0026rsquo;s reliance on a single commodity limits the potential benefits of trade.\u003c/p\u003e \u003cp\u003eThe coefficient of FDI D(LOGFDI(-1)) of 0.003640 with a probability value of 0.6304, indicates an insignificant impact on per capita income. This finding is consistent with the results of Shido-Ikwu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who also found that FDI had an insignificant impact on Nigeria\u0026rsquo;s economic growth. This may be due to the concentration of FDI in sectors like oil and gas, which have limited linkages with the rest of the economy. Similarly, Yusuff et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that foreign trade and FDI did not positively contribute to Nigeria\u0026rsquo;s economic growth, suggesting that FDI inflows may not always stimulate significant growth in developing economies with weak institutions. The insignificant influence of FDI on per capita income implies that while FDI inflows can be beneficial, their impact may be limited if they are concentrated in sectors with weak linkages to the broader economy.\u003c/p\u003e \u003cp\u003eExchange rate, D(EXCHR(-1)), has a coefficient of -0.000105 and a probability value of 0.0420, indicating that an increase in the exchange rate (depreciation) significantly reduces per capita income. This result supports the findings of Shido-Ikwu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who reported that exchange rate fluctuations had a negative and insignificant impact on Nigeria\u0026rsquo;s economic growth. Similarly, Emehelu (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that exchange rate depreciation negatively impacted economic growth, highlighting the vulnerability of developing economies to currency fluctuations. The result implies that Nigeria\u0026rsquo;s reliance on imports, combined with a weakening currency, leads to higher inflation and reduced purchasing power, ultimately hindering economic growth. This finding suggests that exchange rate fluctuations in the exchange rate can have detrimental effects on economic performance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study reveal that international trade has an \u003cstrong\u003einsignificant impact\u003c/strong\u003e on Nigeria's economic growth over the study period. While exports show a positive relationship with per capita income, their impact remains statistically insignificant, implying that Nigeria’s export structure, heavily reliant on crude oil, does not fully contribute to growth. Similarly, imports exhibit a negative but insignificant effect on per capita income, reinforcing concerns that the composition of imports, primarily consumer goods, does not stimulate domestic production or economic expansion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor international trade to have a significant and positive effect on economic growth in Nigeria, the following strategic policy recommendations should be implemented to enhance trade and investment frameworks, as well as stabilizing macroeconomic fundamentals such as the exchange rate:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe government should focus on promoting non-oil sectors such as agriculture, manufacturing, and technology by providing incentives and support for producers to engage in international markets. This will create a more resilient economy less vulnerable to global oil price fluctuations.\u003c/li\u003e\n \u003cli\u003eThe government should revise import policies to prioritize the importation of capital goods and machinery that enhance local production to strengthen its industrial base and reduce reliance on consumer goods imports that do not contribute meaningfully to growth.\u003c/li\u003e\n \u003cli\u003eThe government should implement policies that will stabilize the exchange rate and reduce exchange rate volatility through managing foreign reserves effectively and supporting local industries to reduce import dependency.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe government should implement trade policy reforms that simplify procedures and reduce trade barriers to expand market access, foster competition, and increase Nigeria’s integration into global supply chains.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate: \u003c/strong\u003eThis study did not involve human participants, animals, or sensitive data requiring ethical approval. As such, no ethical approval was necessary. However, the research adhered to all applicable institutional guidelines and best practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e\u0026nbsp;The manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos) that would require consent to publish. Therefore, consent for publication is not applicable in this case.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbebefe, H. A. (1995). The structure of Nigeria\u0026rsquo;s external trade. \u003cem\u003eCentral Bank of Nigeria Bullion\u003c/em\u003e, 19(4), October/December.\u003c/li\u003e\n \u003cli\u003eAcemoglu, D., Johnson, S., \u0026amp; Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. \u003cem\u003eAmerican Economic Review, 91(5) https://doi.org/10.1257/aer.91.5.1369\u003c/em\u003e, 1369\u0026ndash;1401.\u003c/li\u003e\n \u003cli\u003eAdeleye, J. O., Adeteye, O. S., \u0026amp; Adewuyi, M. O. (2015). Impact of international trade on economic growth in Nigeria. \u003cem\u003eInternational Journal of Financial Research (IJFR), 6(3)\u003c/em\u003e, 23-30.\u003c/li\u003e\n \u003cli\u003eAfolabi, B., Danladi, J. D., \u0026amp; Azeez, M. I. (2017). International trade and economic growth in Nigeria. . \u003cem\u003eGlobal Journal of Human-Social Science: Economics, 17(5)\u003c/em\u003e, 1-12.\u003c/li\u003e\n \u003cli\u003eAppleyard, D. R., \u0026amp; Field, A. J. (1998). \u003cem\u003eInternational economics: Trade theory and policy (3rd ed.).\u003c/em\u003e Irwin/McGraw-Hill.\u003c/li\u003e\n \u003cli\u003eArodoye, N. L., \u0026amp; Iyoha, M. A. (2014). Foreign trade-economic growth nexus: Evidence from Nigeria. \u003cem\u003eCBN Journal of Applied Statistics, 5(1)\u003c/em\u003e, 23-31.\u003c/li\u003e\n \u003cli\u003eCollier, P. (2007). The bottom billion. \u003cem\u003eECONOMIC REVIEW-DEDDINGTON 25(1)\u003c/em\u003e, 17-19.\u003c/li\u003e\n \u003cli\u003eDe Matteis, A. (2004). International trade and economic growth in a global environment. \u003cem\u003eJournal of International Development, 16(4)\u003c/em\u003e, 575-588.\u003c/li\u003e\n \u003cli\u003eEmehelu, C. I. (2021). Effects of international trade on the economic growth of Nigeria. \u003cem\u003eInternational Journal of Innovative Finance and Economics Research, 9(1)\u003c/em\u003e, 144-157.\u003c/li\u003e\n \u003cli\u003eEzindu, O. N., Nkechi, O. J., Victoria, O. I., \u0026amp; Chike, U. R. (2020). Impact of international trade on Nigerian economic growth: Evidence from oil terms of trade. \u003cem\u003eInternational Journal of Economics and Financial Management, 5(2)\u003c/em\u003e, 31-47.\u003c/li\u003e\n \u003cli\u003eHeckscher, E. F., \u0026amp; Ohlin, B. (1991). \u003cem\u003eHeckscher-Ohlin trade theory.\u003c/em\u003e MIT: H. Flam \u0026amp; M. J. Flanders, Eds.\u003c/li\u003e\n \u003cli\u003eIngram, J. C., \u0026amp; Dunn, R. M. (1993). \u003cem\u003eInternational economics.\u003c/em\u003e John Wiley and Sons Inc.\u003c/li\u003e\n \u003cli\u003eInternationalMonertaryFund. (2020). \u003cem\u003eIMF Annual Report 2020: Supporting a resilient recovery.\u003c/em\u003e IMF. https://www.imf.org/en/Publications.\u003c/li\u003e\n \u003cli\u003eKeho, Y., \u0026amp; Wang, M. (2017). The impact of trade openness on economic growth: The case of Cote d\u0026rsquo;Ivoire. \u003cem\u003eCogent Economics \u0026amp; Finance, 5(1)\u003c/em\u003e, 1-14.\u003c/li\u003e\n \u003cli\u003eKrugman, P., \u0026amp; Obstfeld, M. (2020). \u003cem\u003eInternational economics: Theory and policy (11th ed.).\u003c/em\u003e Pearson.\u003c/li\u003e\n \u003cli\u003eLin, J. Y. (2001). \u003cem\u003eDemystifying the Chinese economy.\u003c/em\u003e Cambridge University Press.\u003c/li\u003e\n \u003cli\u003eLipsey, R. G. (1986). Successes and failures in the transformation of economics. \u003cem\u003eJournal of Economic Methodology, 8(2)\u003c/em\u003e, 169-201.\u003c/li\u003e\n \u003cli\u003eMannur, H. G. (1995). The principle of trading II. \u003cem\u003eInternational Economics. Second Revised Edition. New Delhi, India\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMike, I. O., \u0026amp; Okojie, I. E. (2012). An empirical analysis of the impact of trade on economic growth in Nigeria. \u003cem\u003eJournal of Developing Societies, 5(6)\u003c/em\u003e, 77-82.\u003c/li\u003e\n \u003cli\u003eNordhaus, W. D. (2002). \u003cem\u003eThe health of nations: the contribution of improved health to living standards (Vol. 8818).\u003c/em\u003e Cambridge: National Bureau of Economic Research.\u003c/li\u003e\n \u003cli\u003eOmoke, P. C., \u0026amp; Opuala\u0026ndash;Charles, S. (2021). Trade openness and economic growth nexus: Exploring the role of institutional quality in Nigeria. \u003cem\u003eCogent Economics \u0026amp; Finance, 9(1)\u003c/em\u003e, 1-17.\u003c/li\u003e\n \u003cli\u003eRicardo, D. (1821). \u003cem\u003eOn the principles of political economy.\u003c/em\u003e London: J. Murray.\u003c/li\u003e\n \u003cli\u003eRodrik, D. (2011). \u003cem\u003eThe globalization paradox: why global markets, states, and democracy can\u0026apos;t coexist.\u003c/em\u003e Oxford University Press.\u003c/li\u003e\n \u003cli\u003eRodrik, D. (2018). \u003cem\u003eStraight talk on trade: Ideas for a sane world economy.\u003c/em\u003e Princeton University Press.\u003c/li\u003e\n \u003cli\u003eShido-Ikwu, S. B., Dankumo, A. M., Pius, F. M., \u0026amp; Fazing, E. Y. (2023). Impact of international trade on economic growth in Nigeria. \u003cem\u003eLafia Journal of Economics and Management Sciences, 8\u003c/em\u003e, 212-226.\u003c/li\u003e\n \u003cli\u003eSmith, A. (1776). \u003cem\u003eAn inquiry into the nature and causes of the wealth of nations.\u003c/em\u003e London: W. Strahan and T. Cadell.\u003c/li\u003e\n \u003cli\u003eStiglitz, J. E. (2002). Information and the Change in the Paradigm in Economics. . \u003cem\u003eAmerican economic review, 92(3)\u003c/em\u003e, 460-501.\u003c/li\u003e\n \u003cli\u003eSun, P. \u0026amp;. (2010). \u003cem\u003eInternational trade and its effects on economic growth in China.\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWorld-Trade-Organization. (2018). \u003cem\u003eThe future of world trade: How digital technologies are transforming global commerce.\u003c/em\u003e Geneva: WTO Publications https://www.wto.org.\u003c/li\u003e\n \u003cli\u003eYusuff, S., Adekanye, T., \u0026amp; Babalola, O. A. (2020). International trade and its effect on economic growth in Nigeria (1986-2017). \u003cem\u003eAmerican Journal of Economics, 4(2)\u003c/em\u003e, 70-85.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Imports, exports, trade barrier, per-capita income","lastPublishedDoi":"10.21203/rs.3.rs-5904994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5904994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study investigates the relationship between international trade and economic growth in Nigeria from 1981 to 2022. The specific objective was to assess the impact of total imports, and\u0026nbsp;total exports on per capita income. Data on total imports, exports (proxies for international trade), and per capita income (proxy for economic growth) were obtained from the World Bank Development Indicators (WDI). The stationarity of the data was tested using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The appropriate lag lengths were determined using lag length selection criteria. The ARDL bound test was employed to assess the long-run relationships among the variables. The Error Correction Model (ECM) was applied to estimate the coefficients in the\u0026nbsp;model. The results of the ADF and PP tests showed that the variables were stationary at mixed levels, both at level I\u003csub\u003e0\u003c/sub\u003e and first difference I\u003csub\u003e1\u003c/sub\u003e. A one-period lag was selected based on the lag length criteria. The ARDL bound test revealed significant long-run equilibrium relationships in the Model. The ECM results revealed that imports had an insignificant negative effect on per capita income (coefficient = -0.017954, p = 0.4627), while exports had an insignificant positive effect (coefficient = 0.004460, p = 0.8352). The study concluded that international trade had an insignificant impact on Nigeria's economic growth over the study period and recommends that the\u0026nbsp;government should implement trade policy reforms that simplify procedures and reduce trade barriers to expand market access and increase Nigeria’s integration into global supply chains.\u003c/p\u003e","manuscriptTitle":"International Trade and Growth in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 08:50:49","doi":"10.21203/rs.3.rs-5904994/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15916a94-ad45-4f0d-ac1b-d7cc67010b36","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T08:50:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 08:50:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5904994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5904994","identity":"rs-5904994","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}